Udacity for Self Driving Cars Engineer

自动驾驶工程师
begin date: 01/11/2021 end date: 31/12/2021

1. Welcome to Self Driving Car Engineer Nano-degree

Lesson 1: An Introduction to your nano-degree program

Lesson 2: Getting help

What it Takes

Completing a Udacity program takes perseverance and dedication, but the rewards outweigh the challenges. Throughout your program, you will develop and demonstrate specific skills that will serve you for a lifetime. Congratulations on taking the first step towards developing the skills you need to power your career through tech education!

The videos, text lessons, and quizzes you encounter in the classroom are optional but recommended. The project at the end of this course will test your ability to apply the skills and strategies you have learned in the classroom to real-world problems. It will also provide tangible outputs you can use to demonstrate your skills for current and future employers.

The project is designed to be challenging. Many students initially struggle, but with a little grit, they are able to learn from their mistakes and build their skills. Data from nearly 100,000 Udacity graduates show that commitment and persistence are the highest predictors of whether or not a student will graduate.

At some point, nearly every student will get stuck on a new concept or skill, and doubt may set in. Don’t panic. Don’t quit. Be patient, and work through the problem. Remember that you are not alone and the problem that you are encountering is likely one that many others have experienced as well. Whether you are stuck or simply looking for encouragement, you’ll find Udacity Mentors and students there to help.

完成一个大胆计划需要毅力和奉献精神,但回报大于挑战。在整个计划中,您将培养和展示为您服务一生的具体技能。祝贺您迈出第一步,通过技术教育发展您的事业所需的技能!

您在课堂上遇到的视频、课文课程和测验是可选的,但建议使用。本课程结束时的项目将测试您将课堂中学到的技能和策略应用于实际问题的能力。它还将提供有形的产出,您可以使用这些产出来为当前和未来的雇主展示您的技能。

该项目的设计具有挑战性。许多学生最初都在挣扎,但稍有勇气,他们就能从错误中吸取教训,并培养自己的技能。来自近10万名Udacity毕业生的数据显示,承诺和坚持是学生能否毕业的最高预测因素。

在某些时候,几乎每个学生都会陷入一个新的概念或技能,怀疑可能会开始。不要惊慌。不要放弃。要有耐心,并努力解决这个问题。请记住,你并不孤单,你遇到的问题很可能是其他许多人也经历过的问题。无论你是被卡住还是只是寻求鼓励,你都会在那里找到大胆的导师和学生来帮忙。

Getting Help

Lesson 3: 认识 Waymo

2. Computer Vision

Lesson 1: Introduction to Deep Learning for Computer Vision

  • Why CV Is import for SDC

  • When to Use Deep Learning for Computer Vision

  • History of Deep Learning

  • TensorFlow

  • Register for the Waymo Open Dataset
    注册Waymo 开放数据集

  • Tools, Environment & Dependencies

  • Project : Object Detection in an Urban Environment

  • Recap 回顾

  • Alt text

    Lesson 2: The Machine Learning Workflow

    Lesson 3: Sensor and Camera Calibration

    Lesson 4: From Linear Regression to Feedforward Neural Networks

    Lesson 5: Image Classification with CNNs

    Lesson 6: Object Detection in Images

    Project: Object Detection in an Urban Environment

    Use the Waymo dataset to detect objects in an urban environment.

    3. Sensor Fusion

    Lesson 1: Introduction to Sensor Fusion and Perception

    作为扫描 LiDAR 的替代方案,还有非扫描传感器,也称为Flash LiDAR。术语“闪光”指的是视场完全由激光源照亮,就像带有闪光灯的相机一样,而光电探测器阵列同时接收反射的激光脉冲。

    Flash LiDAR 传感器没有任何移动部件,这就是为什么它们抗振动并且封装尺寸比扫描 LiDAR 传感器小得多。与屋顶安装的 LiDAR 类型相比,这种传感器类型的缺点是范围有限且视野相对较窄。在自动驾驶汽车中,扫描和非扫描 LiDAR 都用于观察车辆周围的不同区域:安装在车顶的扫描 LiDAR 可生成 360 度视图,直到大约 80-100m 而非扫描 LiDAR 传感器(通常安装在四个角落)在顶部安装的传感器盲区观察车辆的直接附近。

    其他传感器类型
    除了摄像头、雷达和 LiDAR,还有其他类型的传感器可用,例如超声波传感器(自 1990 年代以来广泛用于停车应用)或立体摄像头(有时也称为伪 LiDAR)。但是,这些传感器超出了本课程的范围。从传感器融合的角度来看,将摄像头传感器与 LiDAR 或雷达或两者结合使用是最有意义的,以获得可靠且准确的车辆周围环境重建。

    Lesson 2: The Lidar Sensor

    Lesson 3: Detecting Objects in Lidar

    Mid-term Project: 3D Object Detection

    4. Localization

    5. Planning

    Glossary (术语表)